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Garde, A., Sörnmo, L., Laguna, P., Jané, R., Benito, S., Bayés-Genís, A., Giraldo, B. F., (2017). Assessment of respiratory flow cycle morphology in patients with chronic heart failure Medical and Biological Engineering and Computing , 55, (2), 245-255

Breathing pattern as periodic breathing (PB) in chronic heart failure (CHF) is associated with poor prognosis and high mortality risk. This work investigates the significance of a number of time domain parameters for characterizing respiratory flow cycle morphology in patients with CHF. Thus, our primary goal is to detect PB pattern and identify patients at higher risk. In addition, differences in respiratory flow cycle morphology between CHF patients (with and without PB) and healthy subjects are studied. Differences between these parameters are assessed by investigating the following three classification issues: CHF patients with PB versus with non-periodic breathing (nPB), CHF patients (both PB and nPB) versus healthy subjects, and nPB patients versus healthy subjects. Twenty-six CHF patients (8/18 with PB/nPB) and 35 healthy subjects are studied. The results show that the maximal expiratory flow interval is shorter and with lower dispersion in CHF patients than in healthy subjects. The flow slopes are much steeper in CHF patients, especially for PB. Both inspiration and expiration durations are reduced in CHF patients, mostly for PB. Using the classification and regression tree technique, the most discriminant parameters are selected. For signals shorter than 1 min, the time domain parameters produce better results than the spectral parameters, with accuracies for each classification of 82/78, 89/85, and 91/89 %, respectively. It is concluded that morphologic analysis in the time domain is useful, especially when short signals are analyzed.

Keywords: Chronic heart failure, Ensemble average, Periodic and non-periodic breathing, Respiratory pattern


Giraldo, B. F., Chaparro, J. A., Caminal, P., Benito, S., (2013). Characterization of the respiratory pattern variability of patients with different pressure support levels Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference of the IEEE , IEEE (Osaka, Japan) , 3849-3852

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation


Giraldo, B.F., Gaspar, B.W., Caminal, P., Benito, S., (2012). Analysis of roots in ARMA model for the classification of patients on weaning trials Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 698-701

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials


Chaparro, J.A., Giraldo, B.F., Caminal, P., Benito, S., (2012). Performance of respiratory pattern parameters in classifiers for predict weaning process Engineering in Medicine and Biology Society (EMBC) 34th Annual International Conference of the IEEE , IEEE (San Diego, USA) , 4349-4352

Weaning trials process of patients in intensive care units is a complex clinical procedure. 153 patients under extubation process (T-tube test) were studied: 94 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 21 patients with successful test but that had to be reintubated before 48 hours (group R). The respiratory pattern of each patient was characterized through the following time series: inspiratory time (TI), expiratory time (TE), breathing cycle duration (TTot), tidal volume (VT), inspiratory fraction (TI/TTot), half inspired flow (VT/TI), and rapid shallow index (f/VT), where f is respiratory rate. Using techniques as autoregressive models (AR), autoregressive moving average models (ARMA) and autoregressive models with exogenous input (ARX), the most relevant parameters of the respiratory pattern were obtained. We proposed the evaluation of these parameters using classifiers as logistic regression (LR), linear discriminant analysis (LDA), support vector machines (SVM) and classification and regression tree (CART) to discriminate between patients from groups S, F and R. An accuracy of 93% (98% sensitivity and 82% specificity) has been obtained using CART classification.

Keywords: Accuracy, Indexes, Logistics, Regression tree analysis, Support vector machines, Time series analysis, Autoregressive moving average processes, Medical signal processing, Pattern classification, Pneumodynamics, Regression analysis, Sensitivity, Signal classification, Support vector machines, Time series, SVM, T-tube testing, Autoregressive models-with-exogenous input, Autoregressive moving average models, Breathing cycle duration, Classification-and-regression tree, Expiratory time, Extubation process, Half inspired flow, Inspiratory fraction, Inspiratory time, Intensive care units, Linear discriminant analysis, Logistic regression, Rapid shallow index, Respiratory pattern parameter performance, Sensitivity, Spontaneous breathing, Support vector machines, Tidal volume, Time 48 hr, Time series, Weaning process classifiers


Valente, T., Gella, A., Fernàndez-Busquets, X., Unzeta, M., Durany, N., (2010). Immunohistochemical analysis of human brain suggests pathological synergism of Alzheimer's disease and diabetes mellitus Neurobiology of Disease , 37, (1), 67-76

It has been extensively reported that diabetes mellitus (DM) patients have a higher risk of developing Alzheimer's disease (AD). but a mechanistic connection between both pathologies has not been provided so far Carbohydrate-derived advanced glycation endproducts (AGEs) have been implicated in the chronic complications of DM and have been reported to play an important role in the pathogenesis of AD. The earliest histopathological manifestation of AD is the apparition of extracellular aggregates of the amyloid beta peptide (A beta). To investigate possible correlations between AGEs and A beta aggregates with both pathologies. we have performed an immuhistochemical study in human post-mortem samples of AD, AD with diabetes (ADD). diabetic and nondemented controls ADD brains showed increased number of A beta dense plaques and receptor for AGEs (RACE)-positive and Tau-positive cells, higher AGEs levels and major microglial activation, compared to AD brain. Our results indicate that ADD patients present a significant increase of cell damage through a RAGE-dependent mechanism, suggesting that AGEs may promote the generation of an oxidative stress vicious cycle, which can explain the severe progression of patients with both pathologies.

Keywords: Abeta, Alzheimer's disease, Rage, Ages, Diabetes, Immunohistochemistry, Advanced glycation endproducts, Beta-amyloid peptide, End-products, Oxidative stress, Advanced glycosylation, Synaptic dysfunction, Cross-linking


Perera, A., Pardo, A., Barrettino, D., Hierlermann, A., Marco, S., (2010). Evaluation of fish spoilage by means of a single metal oxide sensor under temperature modulation Sensors and Actuators B: Chemical 146, (2), 477-482

In this paper the feasibility of using metal oxide gas sensor technology for evaluating spoilage process for sea bream (Sparus aurata) is explored. It is shown that a single sensor under temperature modulation is able to find a correlation with the fish spoilage process. Results are obtained in real frigorific storage conditions: that is, at low measurement temperatures with variations of relative humidity.

Keywords: Gas sensors, Electronic nose, Spoilage process, Temperature modulation, Bream sparus-aurata, Electronic nose, Freshness, Quality, Sardines, Storage


Tarzan-Lorente, M., Gutierrez-Galvez, A., Martinez, D., Marco, S., (2010). A biologically inspired associative memory for artificial olfaction Practica 2010 International Joint Conference on Neural Networks (IJCNN 2010) , IEEE, Piscataway, NJ, USA (Barcelona, Spain) , 6 pp.

In this paper, we propose a biologically inspired architecture for a Hopfield-like associative memory applied to artificial olfaction. The proposed algorithm captures the projection between two neural layers of the insect olfactory system (Antennal Lobe and Mushroom Body) with a kernel based projection. We have tested its classification performance as a function of the size of the training set and the time elapsed since training and compared it with that obtained with a Support Vector Machine.

Keywords: Biocomputing, Chemioception, Content-addressable storage, Hopfield neural nets, Support vector machines


Fonollosa, J., Halford, B., Fonseca, L., Santander, J., Udina, S., Moreno, M., Hildenbrand, J., Wöllenstein, J., Marco, S., (2009). Ethylene optical spectrometer for apple ripening monitoring in controlled atmosphere store-houses Sensors and Actuators B: Chemical 136, (2), 546-554

In today's store-houses the ripening of fruit is controlled by managing the ethylene concentration in the ambient atmosphere. Precise and continuous ethylene monitoring is very advantageous since low ethylene concentrations are produced by the fruit itself and are indicative of its ripeness, and on other occasions, ethylene is externally added when ripeness or degreening of the product must be promoted. In this work, a multichannel mid-infrared spectrometer for ethylene measurement is built and characterized. The instrument contains additional channels to reject potential cross-interferences like ammonia and ethanol. Additionally, these channels are useful for monitoring a potential malfunction of the cooling system and possible fouling of the fruit, respectively. The complete spectrometer contains a silicon-based macroporous infrared (IR) emitter, a miniaturized long path cell (white cell), a four-channel detector module, low-noise analog amplification and filtering, and a microcontroller-based lock-in amplifier. The new inner architecture of the detector module features a fourfold thermopile array with narrow band optical filters attached by flip-chip technology, and a Fresnel lens array attached on the lid of the package. Laboratory tests show that the system is able to distinguish between ammonia and ethylene, featuring a detection limit of 30 ppm and 160 ppm (95% confidence) for ethylene and ammonia, respectively. Field tests show that the spectrometer is suitable as an ethylene alarm to detect fruit ripening and prevent fruit to decline into senescence. Simulation results show that system selectivity could be improved by setting ammonia channel to another absorption wavelength.

Keywords: IR spectrometer, Ethylene, Fruit storage, Fresnel lens, White cell, Lock-in amplifier